Greetings everyone. I’m still in moving mode, packing up life in Texas and getting ready for a year in Boston. I hit the road on Friday of this week for a three day road trip and am still behind on everything. That means the podcast is still on reruns for now, but I should have a new episode for you next time. This week’s rerun is one I really liked, though—my conversation from two years ago with Steven Berry.

Steven is the Sterling Professor of Economics at Yale and the inaugural Faculty Director of the Tobin Center. His work in industrial organization has shaped how economists think about markets in equilibrium, and his research spans industries from autos to airlines to media. He’s also a winner of the Frisch Medal, a member of the National Academy of Sciences, and one of the field’s most respected voices.

We talked about his path into economics—from the Midwest, to Wisconsin, to a career that’s helped define modern empirical IO. Naturally, we dug into the BLP model, the landmark framework he developed with James Levinsohn and Ariel Pakes that changed how we estimate demand in differentiated product markets. It’s one of those ideas that’s both deeply technical and hugely practical in policy and business.

If you missed it the first time, I think you’ll enjoy hearing Steven reflect on his career, his collaborators, and where the field is headed. Here’s my rerun conversation with Steven Berry.

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I’m still going through some older reruns for the summer due to my travel schedule. This one is an interview with Rocío Titiunik, a quantitative methods political scientist and professor in the department of politics at Princeton University, as well as a researcher that has been at the frontier of work on regression discontinuity designs.

Her name is synonymous with cutting-edge work on regression discontinuity design, developed in close collaboration with scholars like Sebastián Calonico, Matías Cattaneo, and Max Farrell. Together, they’ve shaped the modern landscape of causal inference, not only through groundbreaking theory but also through widely used software tools in R, Stata, and Python. In addition to her contributions to quantitative methodology, Rocío’s applied research — from electoral behavior to democratic institutions — has become a major voice in political science. She also holds a formidable editorial footprint: associate editor for Science Advances, Political Analysis, and the American Journal of Political Science, and APSR. It’s no exaggeration to say she helps steer the field as much as she contributes to it.

In this older interview, Rocío shared how her journey into economics began not with data, but with theory, literature, and the big questions that led her to the discipline. Her path into Berkeley’s PhD program in agricultural and resource economics was anything but linear, and even once there, she wasn’t sure how all the parts of herself — the scholar, the immigrant, the thinker — would fit together. During our conversation, she opened up about moments of uncertainty, of feeling lost in the sheer vastness of academic economics. Her honesty was disarming. It reminded me that no matter how decorated someone’s résumé may be, we’re all just trying to find our way — and sometimes, the most important breakthroughs happen when we admit we haven’t arrived yet.

Thanks again for tuning in! I hope you like listening to this older podcast interview.

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Greetings from San Sebastián Spain where I am on holiday with my daughter for another couple of weeks. I have still not done any new podcasts as I realized only after I left that I did not pack my microphone. And, I didn’t want to buy a new one, and I wasn’t really 100% positive if using my Apple AirPods would work well. All of that is to say — excuses.

So, this week we are going back down memory lane to an interview I did 1-2 years ago with one of my favorite young up and coming econometricians, Tymon Słoczyńsi from Brandeis University. Tymon is the author of a wonderful 2022 article on OLS models with, I’ll call it, “additive and separable” covariates under unconfoundedness. Autocorrect wanted that to be “addictive” instead of “additive”, which would’ve been a really clever Freudian slip.

Tymon’s interview was one of my favorites. I know I say that about every interview, but they all feel like that, but let’s just this one really really feels that way. And I think you’ll feel the same way.

One of the things I love about Tymon’s articles is how excellent the writing is. His paragraphs oftentimes feel like the kind of paragraphs that you can tell he wrote, and rewrote, and rewrote, and rewrote like a hundred times. It amazes me that English is not his first language and he writes this well. I don’t even mean this clear — I mean it’s beautiful writing. Here’s a paragraph I think is outstanding, for instance:

“To aid intuition for this surprising result, recall that an important motivation for using the model in equation (1) and OLS is that the linear projection of y on d and X provides the best linear predictor of y given d and X (Angrist & Pischke, 2009). However, if our goal is to conduct causal inference, then this is not, in fact, a good reason to use this method. Ordinary least squares is “best” in predicting actual outcomes, but causal inference is about predicting missing outcomes, defined as ym = y(1) × (1− d ) + y(0) × d. In other words, the OLS weights are optimal for predicting “what is.” Instead, we are interested in predicting “what would be” if treatment were assigned differently.”

A lot of his sentences are sentences that are so precise, so insightful, that I wish I could have written it. It’s superb, he’s superb, and if you haven’t listened to this, I hope you do, and if you already have listened to it, then I hope you listen to it again.

Thanks again for all your support. Wish me luck as I wrap up my summer in Europe, start making my plans to move to Boston, teach new students, meet new colleagues, and make new friends. And get some new clothes to replace the ones the gentleman who stole my luggage on the train in Switzerland is now in possession of.

Scott's Mixtape Substack is a reader-supported publication. To receive new posts and support my work, consider becoming a free or paid subscriber.